Stacker + Status Panel Series 01: How to Set Up OpenClaw on Real Servers, Not Just on Your Laptop

Why this article matters

A lot of people can get an AI tool running locally once.

The harder question is:

How do I turn that into a repeatable setup that works on a real remote server, stays observable, and can be changed safely later?

That is exactly where a stack workflow plus Status Panel becomes useful.

Instead of copying random Docker Compose fragments and hoping for the best, you can move through a cleaner sequence:

1. prepare the project locally
2. generate or refine stacker.yml with AI
3. deploy to a remote server
4. check the result

Before you run the commands

If you do not have Stacker installed yet, the quickest path is the install script.

curl -fsSL https://raw.githubusercontent.com/trydirect/stacker/main/install.sh | bash

GitHub repository and installation steps

If you prefer to explore Stacker with Docker first, you can also pull the image:

docker pull trydirect/stacker:latest

If your workflow also uses Status Panel locally or on a target server, start here too.

curl -sSfL https://raw.githubusercontent.com/trydirect/status/master/install.sh | sh

GitHub repository and installation steps for Status Panel

docker pull trydirect/status:latest

1. monitor apps and logs
2. make configuration changes without losing control

This example uses the OpenClaw + n8n stack because it shows a realistic AI automation system, not just a single container.

What is in the OpenClaw + n8n stack

The repository already describes the stack as a self-hosted AI assistant plus workflow automation.

Typical services include:

• OpenClaw for the AI assistant interface and integrations
• n8n for workflow automation
• PostgreSQL for workflow state
• Redis for queueing and caching
• Qdrant for vector search and knowledge storage
• Ollama for local inference and embeddings

This is why it is a good example. It reflects the kind of multi-service system teams actually want to run for business experiments and internal tooling.

Prerequisites

Before starting, have these ready:

• Stack workflow CLI installed
• Docker available for local verification
• access to a remote Linux server for --target server
• SSH access to that server
• domains or subdomains if you want public URLs
• AI provider credentials, or a decision to stay local with Ollama

Check version first

stacker --version

Outcome of this tutorial

By the end, you should have:

• a reviewed stacker.yml
• a locally testable stack
• a remote deployment
• a working operator checklist for health, logs, and restart flows

Step 1: Start with a local project, not with a random server shell

Create or open your project directory locally first.

The goal here is to define the stack intentionally before you touch production infrastructure.

For a stack-driven workflow, your local folder becomes the working source for:

• stacker.yml
• generated .stacker/ artifacts
• environment references
• service composition
• future deployment reuse

If you already have application code, keep it next to the stack definition so the stack engine can inspect the project context.

Example

mkdir openclaw-workspace

cd openclaw-workspace

Checkpoint

At this point you should be in a clean local directory where stacker.yml and .stacker/ can be generated and reviewed.

Step 2: Generate stacker.yml with AI

One of the most practical stack workflow features is AI-assisted initialization.

You can start with:

stacker init --with-ai

Or choose a provider explicitly:

stacker init --with-ai --ai-provider anthropic

Or:

stacker init --with-ai --ai-provider openai --ai-api-key sk-...

What happens here

Under the hood, the stack engine scans the project context, looks at files such as package.json, requirements.txt, Cargo.toml, Dockerfile, docker-compose.yml, and .env, then proposes a stacker.yml.

In the newer workflow, that local scan can also surface advisory pipe or integration hints before anything is deployed. For example, if the project already suggests PostgreSQL, Redis, Qdrant, a frontend-to-backend API path, webhook usage, or an LLM provider, the AI prompt can use those clues when shaping the initial stack.

The value is not that AI magically finishes the job.

The value is that it gives you a structured starting point with:

• app configuration
• service assumptions
• likely local integrations and pipe hints inferred from the project files
• monitoring hooks such as Status Panel integration and healthcheck assumptions
• deployment-friendly structure

Practical advice

Treat the AI output as a first draft.

Review:

• exposed ports
• service names
• domain assumptions
• environment variables
• whether the suggested local integrations match the real architecture
• whether the AI selected local inference or cloud model settings

If the output does not fit your needs, refine it instead of throwing the whole process away.

Before moving on, it is also worth validating or inspecting the generated config:

stacker config validate

stacker config show

That gives you a cleaner checkpoint before you deploy anything.

Step 3: Add or refine services in stacker.yml

For OpenClaw-style deployments, you may want to confirm that the stack includes the services your use case actually needs.

The stack workflow also supports service templates, so you can extend the stack with known building blocks.

Examples:

stacker service list

stacker service add qdrant

stacker service add redis

stacker service add postgres

This is useful when your initial AI-generated file is close, but not complete.

For example:

• add Qdrant when you want document search or RAG
• add Redis if the workflow requires queueing or caching
• add PostgreSQL when you want more reliable state than local files

At this stage, you are shaping a stack you can explain and maintain later.

Suggested review questions

• do I need only OpenClaw and n8n, or also local embeddings and vector search?
• should this environment use local Ollama first, or a hosted provider?
• which services must stay private?
• which URLs must be public?

Step 4: Prepare configuration before deployment

Before deploying, fill in the environment values your stack actually needs.

For the OpenClaw + n8n stack, that typically includes:

• OPENCLAW_DOMAIN
• N8N_HOST
• POSTGRES_PASSWORD
• N8NENCRYPTIONKEY
• AI provider credentials if you are not using a local-only Ollama path

If your goal is privacy-first experimentation, prefer local inference where it makes sense.

If your goal is fast evaluation of model quality, connect a hosted provider first and optimize later.

The important point is that the stack should be configured intentionally, not by accident.

Example checklist

• set domains
• set passwords and encryption keys
• decide which model path to use
• confirm ports are intentional
• confirm no secrets are hardcoded in committed files

Checkpoint

Before deploying, you should be able to explain what each externally exposed service is for.

Step 5: Deploy locally or to a remote server

For a quick local run:

stacker deploy

For a remote target:

stacker deploy --target server

If you want to preview generated artifacts before touching a remote machine, use:

stacker deploy --dry-run

This is where the workflow becomes different from one-off Docker experiments.

Instead of hand-running several container commands, the stack engine treats the deployment as a managed stack operation.

Also note a few practical stack workflow behaviors that make iteration easier:

stacker init # now creates the .stacker/ directory up frontstacker deploy # reuses existing .stacker/ artifacts

• local deploys do not require the full Stacker server flow
• remote operations can later be paired with agent-aware commands

That makes iteration cleaner because you are not regenerating everything blindly each time.

Recommended tutorial flow

Use this exact order:

stacker deploy --dry-run

stacker deploy

stacker status

stacker deploy --target server

What success looks like

• local artifacts are generated
• local services start

stacker status # shows expected containers

• remote deployment finishes without forcing you into manual Compose work

Step 6: Check whether the result is actually healthy

A deployed stack is not automatically a healthy stack.

After deployment, perform a basic operational review:

1. Run stacker status to confirm the local or deployed stack is up.
2. Open the OpenClaw UI.
3. Open the n8n UI.
4. Confirm that PostgreSQL-backed services are starting cleanly.
5. Confirm that supporting services like Redis, Ollama, or Qdrant are reachable.
6. Import starter workflows if your use case depends on them.

For this stack, a realistic smoke test looks like:

• connect an AI provider or confirm local inference
• connect one messaging channel
• import one workflow into n8n
• trigger a simple command such as a server-status or notification action

That is better than checking only whether ports respond.

Minimal smoke test

Run through one short scenario:

1. open OpenClaw
2. connect one AI path
3. import one n8n workflow
4. run one message-driven or webhook-driven task
5. confirm the output lands where expected

If this works, you have more than a booted stack. You have an operating baseline.

Step 7: Use Status Panel for monitoring and operational checks

This is where the article becomes more than a deployment tutorial.

The Status Panel is meant to close the gap between deployment and day-2 operations.

It supports the kind of tasks teams actually need:

• container monitoring
• system information
• agent registration
• secure credential handling
• health checks
• log retrieval
• restart operations

If the Status Panel agent is registered with the stack platform, it becomes possible to drive operational actions through a more structured flow instead of ad hoc SSH.

At architecture level, the repo now makes this model very explicit:

• Stacker CLI is the developer-facing entry point
• Stacker Server handles orchestration, API, and command queueing
• Status Panel Agent runs on the target server and executes health, logs, restart, exec, deploy, proxy, and firewall actions

What to check first

• is the deployment agent online?
• which capabilities are available?
• what is the current health of the OpenClaw and n8n containers?
• are there startup or config errors in the logs?

This is the point where teams begin treating the environment like a supported system, not a throwaway experiment.

Step 8: Use stacker agent for practical remote operations

Stacker already includes a growing set of agent-oriented commands for remote operational work.

Examples include:

stacker agent status

stacker agent health --app openclaw

stacker agent logs openclaw --lines 200

stacker agent restart openclaw

You can also use nearby commands when you need more context:

stacker logs --service openclaw --tail 200

stacker status

This matters because it gives you a repeatable pattern for:

• checking app health
• reading logs
• restarting individual containers
• confirming the state of a deployment without custom scripts

And because Status Panel itself is now more than a simple container dashboard. In the status repo it is described as:

• a single-binary infrastructure agent
• Docker-aware
• metrics-aware
• capable of signed remote command execution
• able to run as CLI, daemon, API server, or API+UI server

Practical operator loop

After every remote deploy, repeat this loop:

stacker agent status

stacker agent health --app openclaw

stacker agent logs openclaw --lines 100

stacker agent health --app n8n

stacker agent logs n8n --lines 100

If something fails, restart only the affected app first.

For teams running OpenClaw as part of a real internal workflow, these commands are often more useful than a raw "deployment succeeded" message.

Step 9: Make configuration changes safely

Sooner or later, every useful AI stack changes.

You may need to:

• switch AI providers
• change domains
• add a vector database
• add a reverse proxy rule
• expose one service and keep another internal
• adjust environment variables for n8n or OpenClaw

The right workflow is:

1. update the stack definition or env config locally
2. review the impact
3. redeploy intentionally
4. re-check health and logs
5. document the new stable state

If your change is specifically app configuration and not only stack structure, there is also a more advanced underlying flow in the platform docs:

• frontend or CLI updates configuration intent
• the stack engine stores and versions app config
• rendered config is synchronized to Vault
• Status Panel receives enriched deployment commands with compose, env, and config bundle data
• files are written on the target server before app deployment or restart

That is important because it means configuration changes can become part of a governed workflow instead of only a manual server edit.

This is much safer than patching a live server manually and hoping you remember what changed.

A practical business example

Imagine a small operations team wants an internal AI assistant that can:

• answer basic internal questions
• trigger workflows in n8n
• summarize incoming information
• alert the team when something fails

The first version may start as an experiment.

But once it proves useful, the team needs:

• a known stack definition
• reliable remote deployment
• health visibility
• log access
• a clear path for iterative improvements

That is why a stack workflow plus Status Panel is more valuable than "just run this Compose file."

Final takeaway

If you want to set up OpenClaw for real computers and remote servers, do not stop at "the containers started."

The stronger workflow is:

• define the stack locally
• generate and refine stacker.yml
• deploy intentionally
• check the result
• monitor with Status Panel
• make changes in a controlled way

That is how an AI experiment becomes an operational system your team can trust.

Important limitation to mention honestly

Today, Stacker still follows a 1 deployment : 1 agent : 1 server model.

If you want OpenClaw on more than one machine right now, the practical workaround is multiple deployments under the same project, each with its own:

• deployment_hash
• Status Panel agent instance
• secrets
• command queue
• monitoring state

That is still usable, but it is not yet a single unified multi-server deployment.

Suggested next article

The natural follow-up to this guide is:

Stacker + Status Panel Series 02: How to Monitor and Debug a Live Stack Without Falling Back to SSH for Everything

Key takeaways

• The right workflow starts locally, not on a random remote shell.
• AI-assisted stacker.yml generation is useful when teams review and refine it intentionally.
• OpenClaw-style deployments need health checks, logs, and controlled changes after launch.
• The stack workflow plus Status Panel turns a first deployment into an operational system, not just a running container set.

FAQ

How do I set up OpenClaw on a real server without losing control later?

Start locally, generate and review the stack definition, deploy intentionally, then use Status Panel and the stack operations layer to verify health, inspect logs, and make changes in a controlled way.

Is this workflow only for OpenClaw?

No. OpenClaw is a practical example, but the same approach works for other multi-service AI, automation, or internal tool stacks that need repeatable deployment and day-2 operations.

Why not configure everything directly on the server?

Because that makes reuse, debugging, and change management much harder. A reviewed local stack definition is easier to explain, version, and repeat.

What should I review before deployment?

Review service names, ports, domains, environment variables, inference choices, and whether the generated stack actually matches the architecture you want to run.

What to do next

• test the stack locally or in a safe remote environment first
• verify health, logs, and expected URLs after deployment
• document one safe change cycle for your team
• continue to the monitoring and debugging article before scaling the workflow further